The evolution of high rate data services within future wireless networks will call or new RF access technologies to enable substantial increases in overall system spectral efficiency at an acceptably low cost to the user. This assumption is based on the widely promoted view that data capacity requirements will grow exponentially over the next few years with RF spectrum remaining a scarce commodity.
Space-Time Coding (STC) is an antenna array processing technology currently stimulating considerable interest across the wireless industry. It promises to deliver substantial system benefits in terms of the achievable air interface spectral efficiency, and is considered for future high capacity, wireless data products.
STC exploits both the temporal and spatial dimensions for the construction of coding designs which effectively mitigate fading (for improved power efficiency) and are able to capitalize upon parallel transmission paths within the propagation channel (for improved bandwidth efficiency). The use of multiple propagation paths is fundamental to the concept, and provides the means for better Signal to Noise Ratio (SNR) gain (i.e. power efficiency) and better diversity performance across the radio link. Furthermore, in a rich multipath environment antenna processing can provide access to independent, parallel transmission paths, which are also referred to as ’spatial modes', ’spatial channels' or ’data pipes'.
In FIG. 3 there is shown a plot for the throughput (as defined by Foschini and Gans “On Limits of Wireless Communications in a Fading Environment when using Multiple Antennas“’, Wireless Personal Communications, Vol. 6, 1998, Kluwer Academic Publishers, pp. 311-335) which is exceeded for 90% of frames (known as the ’10% Outage Capacity') from a number of transmit elements (NT) and a number of receive elements (NR) for the cases of (NT,NR) =(1,1), (2, 2), (4,4) and (1,4) over 10,000 random channel realizations for different overall mean SNRs. In order to calculate fundamental per-link capacity bounds, coded data frames of length approaching infinity are assumed, with random, independent, Rayleigh channel realizations for each transmitter-receiver path. Thus an (NR,NT) matrix of complex channel gains, H, can be constructed with independent complex Gaussian elements. A transmitter would have no prior knowledge of the instantaneous channel realizations, and so would send equal-power uncorrelated noise-like waveforms to the different antennas. It can be shown that the fundamental capacity (i.e. maximum throughput for vanishingly small error probability) for such a system would vary from frame-to-frame, as a function of the eigenvalues of the matrix product HHH.
There are large ’outage capacity' increases for NT=NR=N =1,2 and 4. This is due to the combined effects of transmitter/receiver diversity (which draws the ’worst cases' closer to the median cases), receiver SNR power gain, and parallelism. These increases can be considered as a capacity increase at a given receiver SNR, or conversely a reduction in required SNR for a given link capacity. Note that the case of (NT,NR)=(1,4) is much better than (1,1) due to the extra SNR gain and diversity, even though there is no parallelism to be exploited with (1,4). However, there are still additional benefits in going to (4,4) In terms of diversity and parallelism (offering an additional multiplicative factor).
It is commonly accepted that the capacity of CDMA cellular systems is inversely proportional to the working Eb/No of the subscribers. In the reverse-link case, it is the received Eb which must be minimized, whereas for the forward-link our aim is to minimize the transmitted Eb (where Eb denotes energy per information bit). Thus the per-link throughput increases as a logarithmic function of link Eb (in the manner prescribed by Shannon, ’Communication in the Presence of Noise' Proc. IRE, vol. 37, pp. 10-21, Jan. 1949), whereas the number of links which can be supported is inversely proportional to Eb. If we consider the overall network capacity to be the product of the per-link capacity and number of links, then we obtain the characteristic shown in FIG. 4. This shows that systems with larger numbers of links, each working at a lower Eb/No and hence lower throughput, will have a larger network capacity (i.e. higher spectral efficiency) than systems working at a higher link Eb/No, but with fewer links. This is independent of any statistical multiplexing benefits which apply for large numbers of users, and this further favors systems with large numbers of low-SNR/low-throughput links. This analysis relies upon the assumption that the system is not bandwidth limited (i.e. in the case of the CDMA forward link this requires that there be a plentiful supply of Walsh codes).
It can therefore be deduced that, for a CDMA network which is severely interference-limited, every 3dB reduction in Eb/No at the same link throughput and error rate provides a 2-fold capacity benefit. For other system scenarios, for example fixed directional-antenna systems such as Nortel Networks’ Fixed wireless Access product ’Proximity II', or other mobile/nomadic or fixed-wireless applications where there are multiple diversity antennas at the terminal, the arguments are reversed. The interference level is low, but the number of frequency channels/timeslots (TDMA) or orthogonal Walsh codes (CDMA) is limited. System capacity should be enhanced by using more bandwidth-efficient modulation, that is, modulation with a higher spectral efficiency in terms of bps/Hz. In the case of bandwidth-limited systems, every 2-fold increase in coder/modulation spectral efficiency at the same error rate provides a 2-fold capacity benefit. In terms of the capacity vs. SNR curves of FIG. 3, there is a goal to increase the bps/Hz axis.
Two major applications are currently envisaged for STC technology; (i) Low mobility or nomadic high rate wireless terminals (i.e. laptop or Personal Digital Assistant (PDA) versions) which could be expected to operate both outdoors and indoors; (ii) Fixed terminals deploying indoor or outdoor antennas. The propagation channel will differ significantly according to the application scenario in terms of the multipath angle spread and time dispersion. The angle spread phenomenon, In particular, will have significant impact on the link performance gain using Space-Time Coding. The terminal antenna design and its interaction with the surrounding propagation environment also influence the attainable performance gain of the STC configuration. It is envisaged that STC technology will be applied to a range of possible systems including EDGE, 3rd Generation WCDMA, future fixed wireless access, and 4th Generation systems.
Several key implementation issues influence the achievable performance gain using STC. Since STC capitalizes on the inherent parallelism in the spatial channel, one possible risk concerns the substantial increase in DSP load needed to estimate the multiplicity of channel impulse responses at the terminal.
The fundamental principle of STC, is illustrated in FIG. 1. _Coded data symbols are transmitted simultaneously from multiple antennas. This simultaneous transmission of modulation symbols from different antennas is termed a Space-Time Symbol (STS). Since modulation symbols can conventionally be represented by a complex number, an STS can be represented by a vector of complex numbers, with the number of complex elements in the vector equal to the number of transmit antennas. In a Frequency Division Multiple Access/Time Division Multiple Access (FDMA/TDMA) system these simultaneous symbols use the same carrier frequency and same symboling waveform. In a CDMA system—an identical symboling waveform, in the form of an identical spreading code (i.e. Walsh code) is also used.
Assuming a non-dispersive channel, the receiver simultaneously detects all of the elements of a transmitted STS using a single symbol-matched filter per receiver antenna. These detection outputs are built up into a (vector) detection statistic. Thus FIG. 1 shows that every element of this vectorial receiver detection statistic is a superposition of the multiple simultaneous transmissions, as seen at each receiver antenna. This cross-coupling between antenna transmissions can be thought of as a form of intersymbol interference (ISI). Each element, viewed over many STS, will thus resemble a somewhat disorganized Quadrature Amplitude Modulated (QAM) type constellation (in the noice-free case), where the exact form of this constellation depends on the STC encoder structure, STC modulation alphabet and instantaneous channel.
In the case of multiple receive antennas the transmissions from individual transmit antennas, however, may not be totally (spatially) separated at the receiver. This spatial-ISI can be overcome in a number of ways, including; by a form of antenna spatial ’nulling' (analogous to linear ’zero-forcing' time-domain equalization) or spatial Minimum Mean-Squared Error (MMSE) beamforming (analogous to linear MMSE time-domain equalization); or, subtraction (analogous to Decision Feedback time-comain equalization); or, maximum-likelihood sequence estimation (MLSE) of coded Space-Time Symbols (and hence information biyts) from all transmit antennas, based on observations of the vectorial receiver decision statistic over the whole coded frame.
Using conventional methods (and with a bounded receiver complexity), waveform design is inevitably a compromise between bandwidth efficiency and power efficiency. However, apparently good STC systems can seemingly ’side-step' the above barrier (Shannon bound) by achieving increased bandwidth efficiency with little or no reduction in power efficiency. In principle, STC transmissions could be successfully received in a link with only a single receiver antenna. However, the power efficiency would be very poor, because it would not be possible to capitalize on the additional SNR and diversity gain provided by a multiple-antenna terminal design, and due to the lack of multiple parallel decoupled ’data pipes'. Put more simply, and referring again to FIG. 1 (assuming only a single receiver antenna), the receiver detection statistic would only have a single element, and it would resemble an extremely high-order QAM constellation (but which changes both in size and shape as the channel fades at the Doppler rate). This high-order constellation would have a large number of constellation points, all lying in the same 1-dimensional complex space. Hence, like QAM, the power efficiency of such a link would be rather poor. In the limit, two constellation points may even lie on top of one another, and the receiver would require redundancy (memory) in the coding in order to separate them.
There are, of course, other proposed increased capacity transmission and reception techniques besides STC, some of which are alternatives to STC, while others may be complementary. One possible technique is ’fixed beamforming' (FB), which in the case of the BTS implies a higher order of sectorisation. FB can actually be complementary to STC, although if both techniques were to be used simultaneously, the number of antenna elements required would increase multiplicatively.
FIG. 2 illustrates a possible range of applications for Space-Time Coding technology: Low mobility applications, which include pedestrian and nomadic terminals —where terminals would be in the form of PDAs or some other form of personal communicators and laptops, which would be expected to operate in both outdoor and indoor environments; in-building, wireless LAN, where the propagation channels often have a quite limited delay spread but would typically exhibit very rich multipath (angle) scatter; Wideband fixed wireless access (e.g. Multi-channel Multi-point Distribution Service [MMDS]) involves Space-Time Coding and adaptive antenna processing at the subscriber and could be exploited to provide enhanced link SNIR margin to increase system capacity and enable simpler installations using indoor (non-directional) antennas; and in-vehicle, high mobility applications.
In many cases, however, the capacity limits of both interference and bandwidth (Walsh code) have been reached, thus leaving no conventional means of increasing capacity.
It will be noted from the above, that the signal seen at each receive antenna is a superposition of the multiple simultaneous transmissions from each transmit antenna. With reference to the channel model of FIG. 11, (and as described in V. Tarokh, N. Seshadri and A. Calderbank, “Space-time codes for high data rate wireless communication: Performance criterion and Code Construction,” IEEE Trans. Inform. Theory 44(2):744-764, March 1998) at each symbol interval i the transmitted vector x=(x1,x2, . . . ,xt) is received according to y[i]=H[i]x[i]+n[i]. Where y[i] is a sequence of received r-vectors, x[i] is a sequence of transmitted t-vectors, n[i] is a sequence of noise vectors and H is an r x t matrix representing the channel coefficients.
Accurate channel estimation (i.e. estimation of the complex gains between each transmit/receive antenna pair) is important for reliable decoding of space-time codes. This is because the maximum likelihood branch metric M(x,y) for a received vector y and hypothesized transmit symbol x is given byM (x,y)=||y=Hx||22
Thus in a space-time decoder it is necessary to calculate the Euclidean distance between the received vector and what would have been received if x was transmitted. it is only possible to find out what would have been received if the channel gains are known.
The maximum likelihood sequence estimator (implemented by the Viterbi algorithm) outputs the sequence that minimizes the sum of such branch metrics over the window of interest. The channel parameters are required in calculation of these maximum likelihood metrics. If these parameters are not known exactly, the discrepancy between the parameters used and the actual channel parameters has an effect similar to having an increased amount of thermal noise, which degrades performance. This is in contrast to a single antenna additive noise channel, in which knowledge of the signal amplitude or noise variance is not required for the Viterbi algorithm (although it is required for the maximum- a posteriori [MAP] algorithm).
Using the analysis given In V. Tarokh, K Naguib, N. Seshadri and A. Calderbank (“Space-time codes for high data rate wireless communication: Performance criteria in the presence of channel estimation errors, mobility and multiple paths,” IEEE Trans. Commun. 47(2);199-207 Feb. 1999), it can be shown that if the channel uncertainty is modelled as an additive noise term with variance ν on each of the channel gains, a loss in coding gain of approximately10log10(νtγ)dB   (1)is suffered, where t is the number of transmit antennas and γ=Es/N0 is the signal to noise ratio (SNR). This is especially bad since the channel estimation noise variance is amplified by both the number of transmit antennas, and the operating SNR
Since the channel gains H are not known a-priori, they must be estimated. The conventional method is to transmit a known sequence of data called a training sequence, which is used by the receiver to produce estimates of the channel gains. Since the performance of the decoding step depends on the accuracy of these channel estimates, a minimum mean square error estimator (MMSE) may be used. This would minimize the variance of the error on the channel estimates, which in turn minimizes the implementation loss described in Equation 1.
The typical behavior of MMSE estimators is that their error variance decreases with the inverse of the length of the training sequence. In other words, approximately twice as many training symbols are needed in order to halve the mean squared error in the channel estimates. Thus the length of the training sequences has an impact on achievable system performance.
The use of long training sequences, however, goes against the main goal of space-time coding, which as discussed above, is to provide high-rate, spectrally efficient communication. Such training sequences use up power and bandwidth which could instead have been used to transmit data.
In the cdma2000 system, for example, pilot sequences transmitted on orthogonal carriers may be used for the purposes of downlink channel estimation. While the use of pilot sequences appears not to affect the spectral efficiency (since the amount of data transmitted in a packet is unchanged), closer examination indicates that this is not the case. An additional pilot per transmit antenna is required which increases the total transmit power at the base-station. Also, additional Walsh codes must be dedicated to these pilots. Furthermore, the pilot channels produce Interference for users in other cells.
It is therefore desirable to reduce the amount of training symbols and/or the total pilot power required, without affecting the performance of the decoder. This in turn will provide increased capacity.